Digital Commons Network^™

Effects Of Increased Use Of Technology In Early Years Of Child Development, Candy Guadalupe Pena Romero

Capstone Projects and Master's Theses

This senior capstone research project examines how the increased use of technology in early years can have a significant effect on child development, both positive and negative. In this research project, besides conducting an extensive literature review, interviews with three teachers from an early childhood education center and an administrator were interviewed and parent surveys were conducted. The result findings revealed that the increased use of technology in early years affects children’s development. The project also suggests that finding a way to balance technology with traditional play and face-to-face interactions could be a good way to promote healthy development in …

Developing Self-Regulation Skills In Early Childhood, Elizabeth Alvarez

Capstone Projects and Master's Theses

Some elementary-school-age children have not yet developed emotional self-regulation skills. When children have trouble with self-regulation, they have challenges focusing, retaining information, and socializing, which can result in poor academic performance and poor social skills. When children learn emotional self-regulation skills, children can gain emotional intelligence which helps them to recognize, understand, and manage their own emotions. To address this issue, I created a three-day lesson for students in a kindergarten classroom at Sherwood Elementary in Salinas, California.

The Benefits Of Experiential Outdoor Learning For Elementary School Students, Jillian R. Witt

Capstone Projects and Master's Theses

This senior capstone research project aims to delve deeper into the benefits of experiential outdoor learning that contributes to the growing body of knowledge and practical applications of this dynamic educational approach of elementary school students. Through the use of literature review, interviews with program workers and school administrators and survey with educators and past students, the result findings indicated that experiential outdoor learning benefits elementary school students despite some cost challenges, but provided them with more opportunities to learn from outdoor activities to increase their academic achievement, improve their cognition, and enhance their social skills with a deeper sense …

Impact Of Private Early Childhood Education On Academic And Social Preparedness Of Preschoolers, Destiny E. Rizo

Capstone Projects and Master's Theses

Early childhood education plays a crucial role in shaping a child's academic and social development. This senior capstone examines how private preschool programs influence kindergarten readiness in both academic and social domains. Through a literature review and interviews with early childhood educators, this project explores the specific practices within private programs that contribute to children's preparedness. The result findings indicated that smaller class size, individualized instruction, and enriched learning environments are the key factors that could really support early learning success for children. As a future educator, this research highlights the value of incorporating these strategies into public school curriculum …

Exploring The Perception Of Spanish-Speaking Students On Language And Cultural Identity, Andrea Martinez

Capstone Projects and Master's Theses

This senior capstone research project explores the perceptions of Spanish-speaking students regarding the intersection of language and cultural identity within educational settings in Monterey County, California. Spanish speakers represent a significant portion of the student population, therefore understanding their cultural/language experiences is essential for fostering inclusive and culturally responsive educational environments. Drawing on literature about bilingual education, cultural identity, and educational equity, interviews with teachers and surveys of students, the findings revealed how language and cultural identity shape students’ sense of belonging, self-perception, academic engagement, attitudes toward bilingualism, and their cultural expression. In addition, there are challenges these students face …

Impact Of Parental Involvement On Elementary School Student Development In Pursuit Of Higher Education, Valeria Guapo

Capstone Projects and Master's Theses

The purpose of this senior capstone is to examine the relationship between parental involvement and elementary school students' academic trajectories, with a focus on their pursuit of higher education. Through a comprehensive analysis of existing literature and through the use of literature reviews, interviews, and surveys with college students, teachers, and parents, the results revealed that parental involvement is a big part of students' educational performance and mental well-being in many aspects. This research investigates how parental involvement in elementary education influences students' academic achievement, motivation, and aspirations for higher education. The findings highlight the significance of parental involvement in …

Benefits Of Parental Involvement On Kindergarten Through Third Grade (K-3) Students’ Education, Kathie Trujillo-Suarez

Capstone Projects and Master's Theses

The purpose of this senior capstone project is to show the benefits of parental involvement in grades K-3 students’ education. Parental involvement plays an important role in the students’ academic success in the early years of elementary school. Schools, teachers and parents should be aware of the impact parent involvement does to K-3 students. Through the use of teacher interviews, and parent surveys, and literature review, the result findings revealed that parent involvement was a critical component of students' education, leading to higher test scores, and academic achievement in the students' education. Strengthening parental involvement in schools could increase K-3 …

Impact Of Bilingual Education On The Academics Of Elementary School Students, Angelica M. Lopez

Capstone Projects and Master's Theses

This research investigates the impact of bilingual education on the academic performance of elementary school students, with a particular focus on Hispanic students in the Soledad area. As bilingual education continues to grow in importance across the United States, understanding its influence on students' academic outcomes is crucial. This study explores the definition, types, and historical evolution of bilingual education, with emphasis on how bilingual programs affect elementary school students' cognitive development, academic performance, and overall school experience. Utilizing both quantitative and qualitative methods, the research includes surveys administered to teachers and parents, assessing their perspectives on the availability and …

2022 Annual Operations And Maintenance Report 2022 Bres Annual Summary Report For Work Completed In Quadrant 2, Butte-Silver Bow Department Of Reclamation And Environmental Services

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Greening Intelligence: Why Ai Infrastructure And Governance Must Evolve Together, Heng Wang, Poh Seng Lee

Research Collection Yong Pung How School Of Law

AI infrastructure is evolving faster than the regulation and governance needed to ensure it serves public and planetary interests.

Organizational Commitment Of Korean American Nurses: A Cross-Sectional Study, Myunghee Jun, Hye-Kyung Kim, Sungsu Kim, Michael Wreen, Yeon Hee Kim, Christine L. Vandenhouten, Soo Ii Shin

Philosophy Faculty Research and Publications

Background:

Studies on reducing the turnover of foreign-educated nurses (FENs) can help address the nursing shortage in the United States. Organizational commitment and associated factors have been explored in relation to lower turnover. Nursing researchers have identified job satisfaction, psychological contract breach, person-organization fit, and person-work setting fit as major factors in enhancing nurses’ organizational commitment. Research on the organizational commitment of Korean American (KA) nurses would provide insights into reducing turnover among FENs.

Objectives:

This study identifies some of the key factors that significantly enhance the organizational commitment of KA nurses.

Methods:

A cross-sectional study conducted with a survey …

Bioavailability Of Lead In Terre Haute Soils, Terre Haute, Indiana, Zach Kemp

All-Inclusive List of Electronic Theses and Dissertations

Contamination of soils from heavy metal pollution has been a global concern for decades. Heavy metals are released into the soil from natural and anthropogenic releases. The largest producers of heavy metal pollution include mining, industrial production, and the combustion of fossil fuels. Vigo County formed in 1818, and Terre Haute was established in 1832. After the discovery of coal in Clay County in 1867, Terre Haute became the third largest coal producer and fifth largest iron manufacturer in the state during that time. Lead, in particular, is a heavy metal of concern because of the harmful effects to humans …

Air And Missile Defense Threat Scenario Variation To Reduce Pretest Sensitization, Video Games As A Case Study, Julie Renee Szekerczes

All-Inclusive List of Electronic Theses and Dissertations

This study uses fixed and variable video game types to measure pretest sensitization as a proxy for repeated and varied threat test scenarios in system performance testing of air and missile defense systems. The pretest sensitization phenomenon exists when repeated exposure to a test condition influences the participant's response. Research shows air and missile defense development correlates with video games, resulting in similar interfaces and computer operating environments. Department of Defense acquisition test and evaluation results must reflect system performance without prior knowledge of the threat scenarios confounding the results. System performance results inform acquisition decisions, such as further funding …

Energy Forecasting Inaccuracies And Their Direct Impact On Grid Performance, Rashed Khalid Alboom

Theses

Accurate energy forecasting is critical for the stability, efficiency, and cost-effectiveness of modern power grids, particularly as renewable energy sources like solar and wind become more prominent. The variability of these sources presents challenges for traditional forecasting models, which struggle with non-linearity, external dependencies, and evolving grid conditions. These limitations lead to operational inefficiencies, grid instability, and increased financial risks. This study investigates the effectiveness of traditional statistical models, advanced machine learning techniques, and hybrid forecasting approaches to enhance prediction accuracy and grid performance. Using real-world datasets from Kaggle, including historical energy generation, consumption, pricing, and weather variables, this research …

Uvm Based Verification Of Ee621 Risc Processor Module, Collin Neidel

Theses

In the process of digital design, the majority of efforts are focused on verification. This is because a proper verification environment can help engineers discover bugs that otherwise would have flown under the radar. On the other hand, a poorly designed verification environment could fail to fully confirm the device’s functionality and leave the customer with a buggy mess. For obvious reasons, this should be avoided at all costs, hence the emphasis on functional verification and validation efforts. Throughout time, the complexity of the average design under test (DUT) has increased dramatically, making verification a significant challenge for today’s verification …

Enhancing Influence Estimation In Gradient Boosted Decision Trees Through Hierarchical Analysis, Anurag Choubey

Theses

This report presents a novel influence function for gradient-boosted decision trees (GBDTs), a widely-used class of predictive models. Influence estimation aims to quantify how individual training samples affect a model’s predictions, offering valuable insights for model debugging, data quality analysis, and in- terpretability. Existing influence functions for GBDTs—such as LeafInfluence, LeafInfluenceSP, and BoostIn—have shown varying degrees of success, with BoostIn currently recognized as the state-of-the-art in terms of estimation quality and computational efficiency. In this work, we propose BoostInLCA, a new influence function that extends BoostIn by incorporating information from non-leaf nodes via the Lowest Common Ancestor (LCA) path, thereby …

Classification Of Linear Systems Of Equations For Quantum Computing Implementation, Mark Danza

Theses

Drawing nearer to an error-corrected era of quantum computing, it is necessary to understand the suitability of certain post-NISQ algorithms for practical problems. One of the most promising, applicable, and yet difficult to implement in practical terms is the Harrow, Hassidim and Lloyd (HHL) algorithm for linear systems of equations. An enormous number of problems can be expressed as linear systems of equations, from machine learning to fluid dynamics to electrical circuit analysis. However, in most cases, HHL will not be able to provide a practical, reasonable solution to these problems. This work seeks to determine whether problems can be …

Sales Opportunities Lead Qualification In B2b Market, Hajar Hussain Ahli

Theses

This thesis addresses the challenge of inefficient lead qualification in the business-to-business (B2B) market by applying machine learning techniques to predict the likelihood of winning a sales opportunity. Using a real-world dataset of over 78,000 records and 17 variables, the study aims to improve how sales teams identify and prioritize high-conversion leads. A thorough data preparation process was conducted, including handling of missing values, outlier detection, and under-sampling to resolve class imbalance between won and lost opportunities. After thorough data cleaning, preprocessing, and under-sampling to address class imbalance, five machine learning models were developed: Logistic Regression, Random Forest, Neural Network, …

Intellimad: A Framework For Secure Machine Learning Models Evaluation And Fine-Tuning In Federated Setting, Dmitrii Korobeinikov

Theses

In contemporary artificial intelligence (AI) appli- cations, Machine Learning (ML) models are core components enabling AI-driven functionalities, yet selecting and fine-tuning a model and its hyperparameters remains challenging. ML model architecture, as well as key model training parameters, such as the number of training epochs, batch size, and learning rate, are highly dependent on both the dataset modalities and the specific task resolved in a particular application. More sophisticated execution setups may require determination of additional environment-related parameters, such as identifica- tion of computational capabilities required for execution of a particular AI-driven task, or discovery and establishment of desired security-related …

Developing Meld-Accelerated Molecular Dynamics Protocols To Simulate The Binding Of The P53-Derived Ligand To The Mdm-2, X Protein, Maria Ciko

Theses

In this study, we focus on developing computational methods to predict protein-ligand binding affinities, with applications in peptide drug discovery. Molecular Dynamics (MD) simulations can capture the complex conformational behavior of proteins, but their high computational cost limits their efficiency. MELD, or Modeling Employing Limited Data, is a Bayesian approach that integrates external information to accelerate sampling of low- energy, high-probability conformations. Building on previous work by Morrone et al., which successfully applied MELD to P53-MDM2 complexes, we hypothesize that we can effectively compute the relative binding affinities while reducing steric clashes and mitigating the effect of slowed diffusion on …

Predicting The Probability Of Crime Related Danger In Los Angeles, Joshua Oghenetega Okpako

Theses

This thesis examines the use of advanced machine learning to predict crime danger in Los Angeles, where 2023 violent crime rates (503 per 100,000) surpass the national average (363.8 per 100,000). Rooted in theories like social disorganization and victim vulnerability, it addresses the lack of combined victim-centric modeling by focusing on three questions: (1) Do blended ensemble models outperform individual models in predicting crime danger? (2) Can unsupervised learning techniques enhance supervised models’ accuracy through label generation or augmentation? (3) How can we interpret accurate machine learning models' decision-making processes? Using historical crime data from Los Angeles (2020–2025) and various …

Methods Of Quantum Circuit Simulation: A Comprehensive Comparative Analysis, Anthony Peter Bacchetta

Theses

The advent of quantum computing has ushered in a new era of computational capabilities, promising to solve problems that were previously intractable for classical computers. Quantum circuit simulation is an essential aspect of harnessing the potential of quantum algorithms, and understanding quantum systems. This thesis presents an exploration and comparative analysis of various methods used in the simulation of quantum circuits, aimed at providing a comprehensive understanding of their strengths, weaknesses, and practical applications. This starts with a dive into the foundational concepts of quantum circuits, quantum gates, and the fundamental principles underlying quantum computation. Quantum circuit simulation emphasizes the …

Modelling And Load Frequency Control (Lfc) Design Of Microgrid Frame-Worked As Technological Norm To Multi Energy System (Mes), Mekonnen Shewarega Worku

Theses

In today's world, the trend of using renewable energy sources as an alternative energy source is becoming more and more common due to various driving factors, such as energy scarcity (fossil fuel depletion, etc.) and environmental issues (carbon footprint). This trend is leading to a steady increase in the penetration of renewable energy sources (RESs) through microgrid (MG), which is formed from combination of various distribution energy sources (DERs). The inherent intermittent nature of RESs coupled with abrupt load changes can instigate sustained frequency fluctuations and can keep the frequency deviation out of the allowable range that leads to an …

Managing Software Dependency Risks In Web Applications, Christopher Alan Scott

Electronic Theses and Dissertations

Web applications commonly rely on third-party software dependencies to reduce development time. This thesis examines how vulnerabilities in a dependency chain propagate to compromise an application. It analyzes two vulnerable Markdown libraries from the npm and Composer dependency ecosystems, both of which are used for managing packages in applications developed with JavaScript and PHP. The analysis demonstrates how each library’s sanitizing functions—intended for removing unsafe user input when transforming Markdown text to HTML—are defeated to achieve a cross-site scripting exploit and take control of the application. The paper discusses potential business impacts of a compromise, underscoring the need for security …

A Phenomenological Study On Teachers' Lived Experiences With Digital Learning And Students With Disabilities During And Post-Covid-19 Pandemic, Angie R. Tyson

Doctoral Dissertations and Projects

The purpose of this hermeneutic phenomenological study was to describe the experiences of educators with digital learning and students with disabilities during and post-pandemic in various areas in the Satellite Central Regional Area of eastern Georgia. The central research question of the study, "How do teachers describe their experiences with digital learning and students with disabilities during and post-pandemic?" was addressed by considering Bandura's perspectives on the theoretical framework of self-efficacy. Participants were 11 educators with lived experiences of self-efficacy working with students with disabilities and achievement gaps during and post-pandemic. The participants described their experiences in detail through focus …

A Phenomenological Study Of String Teacher Perceptions Of Professional Development At Three Career Stages, Michael James Hanf

Doctoral Dissertations and Projects

This hermeneutic phenomenological study aims to understand the perceptions of elementary and secondary string teachers’ experiences with quality professional development from early-, middle-, and late-career stages. This study analyzes perceptions of quality professional development for music teachers, including content-related, evidence-based, applicable, relevant, collaborative, providing feedback, actively involving teachers in learning, longitudinal, coherent, and allowing for reflection. It follows a hermeneutic phenomenological research design guided by the theory of human motivation and aims to understand how professional development affects teacher needs. Participants include elementary and secondary string teachers across the United States from early-, middle-, and late-career stages. String teachers sampled …

Glacial-Interglacial And Millennial-Scale Changes In Nitrous Oxide Emissions Pathways And Source Regions, J. A. Menking, J. E. Lee, E. J. Brook, J. Schmitt, L. Soussaintjean, H. Fischer, J. Kaiser, A. Rice

Physics Faculty Publications and Presentations

During the transition from the Last Glacial Maximum (LGM) to the Holocene, the atmospheric N2O mole fraction increased by 80 nmol mol−1. Using ice core measurements of N2O isotopomer ratios, we show that this increase was driven by increases in both nitrification and denitrification, with the relative partitioning between both production pathways depending on the assumed isotopic end‐member source signatures. Similarly, we also attribute a 35 nmol mol−1 N2O mole fraction increase during the Heinrich Stadial 4/Dansgaard Oeschger 8 (HS4/DO8) millennial‐scale event to increases in both N2O production pathways. In contrast, the 25 nmol mol−1 N2O mole fraction decrease during …

Relaxation Maximum-Based Iteration Method For Solving The Generalized Absolute Value Equation, Ximing Fang, Zhidong Wang, Zhijun Qiao

School of Mathematical & Statistical Sciences Faculty Publications

The generalized absolute value equation (GAVE) has wide applications in scientific computing. Establishing a high performance computing method to solve the GAVE is a hot research topic in recent years. In this paper, with the aid of the maximum function, the GAVE is decomposed of two equations, and then we present the relaxation maximum-based (RM) iteration method. To see the feasibility of the method, we discuss the necessary and sufficient conditions for the GAVE to have a unique solution. Next, the convergence analysis of the RM iteration is discussed under some convergence conditions. Moreover, some numerical examples of low and …

Global Sporadic-E Prediction And Climatology Using Deep Learning, J. A. Ellis, Daniel J. Emmons, M. B. Cohen

Faculty Publications

Sporadic-E (E_s) is an ionospheric phenomenon defined by strong layers of plasma which may interfere with radio wave propagation. In this work, we develop deep learning models to improve the understanding of E_s, including the presence, intensity and height of the layers. We developed three separate models. The first, building off earlier work in (J. A. Ellis et al., 2024, link in AFIT Scholar, 10.1029/2023sw003669), includes only the main features from radio occultation (RO) measurements. The second adds to that time, date, location, geomagnetic and solar indices, solar winds, x-ray flux, weather and lightning. A …

A Risk Evaluation Framework And Comprehensive Security Compliance Assessment Solution For Space Information Networks (Sin), Bernard Afaare Dawaare

Masters Theses & Doctoral Dissertations

The integration of Internet of Things (IoT) devices into space information networks introduces unprecedented security challenges, necessitating advanced methods for information systems risk assessments. Some proposed approaches in terrestrial networks, predominantly based on Euclidean distance metrics, often fall short of capturing the nuanced and multidimensional nature of risks and vulnerabilities in the unique context of space environments. This paper proposes a novel risk management methodology that leverages both Euclidean distance metrics and probabilistic Monte Carlo-based models for evaluating the likelihood/frequency and impact/severity of vulnerabilities within space IoT systems. Leveraging vulnerability data from established sources such as the NIST and VARIoT …